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Bat Surveys on USFS 

Northern Region 1 Lands in 

Montana: 2006 



Prepared for: 

USDA Forest Service 
Northern Region 



Prepared by: 
Susan Lenard, Bryce A. Maxell, Paul Hendricks and Coburn Currier 



Montana Natural Heritage Program 

Natural Resource Information System 

Montana State Library 



June 2007 




MONTANA 



Natural Heritage 
Program 



Bat Surveys on USFS 

Northern Region 1 Lands in 

Montana: 2006 



Prepared for: 

USDA Forest Service, Northern Region 

P.O. Box 7669 

Missoula, MT 59807 



Agreement Numbers: 

06-CS-11015600-031 

and 
04-CS-11015600-036 



Prepared by: 
Susan Lenard, Bryce A. Maxell, Paul Hendricks and Coburn Currier 




MONTANA 



Natural Heritage 
Program 



>V rtunsiABA /"/jtkk MONTANA 

^^ T htate /!■ ^Natural Resource 

^^ Library Ugjjff Information System 

© 2007 Montana Natural Heritage Program 
P.O. Box 201800 • 1515 East Sixth Avenue • Helena, MT 59620-1800 • 406-444-5354 



This document should be cited as follows: 

Lenard, S., B. A. Maxell, P. Hendricks, and C. Currier. 2007. Bat Surveys on USFS North- 
ern Region 1 Lands in Montana: 2006. Report to the USDA Forest Service, Northern Region. 
Montana Natural Heritage Program, Helena, Montana 23 pp. plus appendices. 

ii 



Executive Su m m a r y 



The distribution and status of bats in Montana 
remain poorly documented on US Forest Service 
Northern Region lands. The Northern Region 
recognized the need for additional documentation 
of bats on Forest Service lands and initiated bat 
surveys in 2005 across the Region on selected 
National Forest (NF) Ranger Districts (RD). In 
Montana, these included Bozeman RD -Gallatin 
NF, Swan Lake RD-Flathead NF, Townsend RD- 
Helena NF, Libby RD -Kootenai NF, and Judith 
RD-Lewis & Clark NF. In 2006, the second year 
of the project, increased number of surveyors in 
the field resulted in greater survey effort with both 
mist-net and acoustic sampling in the following 
RDs: Butte and Dillon RD - Beaverhead-Deerlodge 
NF, Sula and West Fork RD - Bitterroot NF, 
Ashland, Beartooth, and Sioux RD - Custer NF, 
Tally Lake RD-Flathead NF, Helena, Lincoln, and 
Townsend RD-Helena NF, Fortine and Rexford 
RD-Kootenai NF, Mussellshell RD - Lewis & 
Clark NF, and Superior RD - Lolo NF. Following 
a modified protocol based on the Oregon Bat Grid 
system, crews surveyed non-randomly chosen 
suitable habitats within randomly chosen 10 km 2 
sample units in each RD; for a total of 75 sites 
surveyed on Northern Region lands in Montana. 
This approach was primarily targeted at identifying 
species richness within grid cells; inferences on 
rates of occupancy are limited to the percent of 10 
x 10 km ! grid cells where a species was detected 
within each sampled RD. 

The 2006 field survey filled important gaps in 
documented distributions in Montana, adding 
new county records. However, a summary of all 
existing bat records across the region continues 
to show large distribution gaps for all species, 
underscoring the need for additional surveys. 
In particular, large portions of the Beaverhead- 
Deerlodge NF, Custer NF, Flathead NF, Gallatin 
NF, and Lewis and Clark NF lack records for 
any bat species. Even with two years of surveys 
only two Districts (Beartooth RD-Custer NF and 
Libby RD-Kootenai NF) have documented the full 
compliment of species predicted to occur there. 



Ten species of bats were captured by mist net or 
detected by acoustic recording during the USFS 
surveys between late June and early September 
2006. Species recorded included Little Brown 
Myotis (Myotis lucifugus) at 34 sites, Western 
Long-eared Myotis (M. evotis) at 37 sites, Fringed 
Myotis (M. thysanodes) at nine sites, Long-legged 
Myotis (M. volans) at 25 sites, California Myotis 
(M. californicus) at four sites, Western Small- 
footed Myotis (M. ciliolabrum) at 17 sites, Big 
Brown Bat (Eptesicus fuscus) at 23 sites, Hoary Bat 
(Lasiurus cinereus) at 38 sites, Silver-haired Bat 
(Lasionycteris noctivagans) at 28 sites, and Spotted 
Bat (Euderma maculatum) at three sites. California 
Myotis was detected by acoustic recording at 
three sites outside their known distribution; these 
observations are considered tentative until the 
species is captured with mist nets in the area. Call 
analysis has yet to be performed on seven sites. 
Genetic analysis is needed for species identification 
for single individuals netted at three sites. Surveys 
at four sites detected no bats during mist-netting 
efforts; no acoustic sampling was done on these 
sites. 

Tentative identification was made for Yuma Myotis 
at mist-netting sites, but no acoustic recordings 
produced calls definitive for the species and no 
genetic analysis has been performed that confirm 
the species presence in the state. All previously 
recognized observations of Yuma Myotis appear 
to be misidentifications of Little Brown Myotis 
given recent acoustic analysis at a number of 
sites previously identified Yuma Myotis roost 
sites. The presence of this species in the state is 
highly questionable given the lack of definitive 
documentation. 

Detection probabilities for bats with multiple 
survey types (acoustic and mist-netting surveys) 
and survey duration were investigated as a pilot 
project to: (1) compare naive site occupancy rates 
with estimates adjusted because all species are 
not detected at all sites where they are present; 
and (2) plan future inventory and monitoring. 



in 



Models that best fit the resulting data indicated 
that acoustic monitoring generally does a better 
job of detecting most bat species compared to mist 
netting and acoustic surveys outperformed mist- 
net surveys in the number of species documented 
per site. The average naive site occupancy rate 
as determined from acoustic sampling was 38.2% 
while the average naive site occupancy rate as 
determined from mist-netting totaled 18.0%. 
Thus, detection probabilities are clearly higher 
for acoustic sampling methods and allocating 
resources for equipment and supplies to increase 
acoustic monitoring efforts is an important next 
step in monitoring bat species in Montana. Models 
which best fit the data also indicated that duration 
of surveys has an important influence on detection 
of species; although not to the extent of the 
importance of acoustic sampling. Estimates of 
recommended minimum or maximum duration of 
surveys were not a product of this analysis. Naive 
site occupancy rates (range 21.2 to 78.8%) were 
lower than robust estimated occupancy rates (Psi) 
resulting from multiple surveys of grid cells (33.7 
to 100%) for all species for which this comparison 
could be made. 

Lower estimates of detection probability or 
insufficient data for calculation of estimates were 
associated with a number of species with limited 
distributional information. Pilot surveys need to 
be conducted to evaluate baseline levels of site 
occupancy and detection probability for these and 
other bat species in Montana not evaluated with 
this pilot effort. Pilot surveys also need to address 
how detection probabilities vary with sampling 
covariates such as type and duration. This pilot 
survey work will place future inventory and 



monitoring efforts on a sound base for supporting 
management decisions and evaluating changes in 
status. 

We recommend the USFS Northern Region 
continue with a grid-based random sampling 
scheme stratified by ecoregion or Ranger District, 
with multiple surveys per grid cell allowing 
for valid inference of grid cell occupancy rates 
across each sampling stratum. While the Oregon- 
based 10 km 2 grid sampling protocol may be 
appropriate, other grid systems could be employed 
to accomplish landscape-scale bat monitoring. A 
bat sampling grid based upon the latilong concept 
would fit well with other current and historical 
wildlife distribution studies in Montana and would 
greatly simplify implementation of the sampling 
because 1:24,000 scale quadrangle maps fit within 
this scheme and could be used directly as the 
sampling unit. It is important to note, however, 
that the detection analysis shows strong support 
for a grid scale smaller than either the Oregon bat 
scheme or the latilong scheme so that a greater 
number of sample units could be surveyed with 
multiple surveys. Further investigation of the 
appropriate sampling unit and sampling scheme 
is still needed. However, a grid-based sampling 
scheme is an important monitoring approach 
that should be considered beyond USFS lands 
and coordinated with other partner agencies and 
organizations to guide effective bat management 
across the state. 

Up-to-date distribution maps for Montana's species 
can be queried and viewed with a variety of map 
layers on the Montana Natural Heritage Program's 
Tracker website at: http : //m tnhp . org/Tracker . 



IV 



Acknowledgements 



We thank Fred Samson and Jenny Taylor (USFS) 
for initiating and promoting the project through the 
USFS Regional Inventory and Monitoring (RIM) 
program and overseeing its implementation. Jenny 
and Kristi DuBois organized the training session, 
run by Joe Szewczak (Humboldt State University) 
and Pat Ormsbee (USFS). Pat developed the 
sampling grid for survey locations. We thank 
Joe Szewczak for providing additional assistance 
during call analysis and interpretation. 

On-the-ground 2006 surveys were conducted on 
the Beaverhead-Deerlodge NF by Paul Hendricks, 
Coburn Currier, Bryce Maxell, and Susan Lenard 
(MTNHP); on the Bitterroot NF by Nate Schwab 
(USFS), Dave Romero (Bitterroot NF), Scott 
Eggeman, and Joe Butsick; on the Custer NF 
by Barb Pitman and Tawni Parks (Custer NF), 
Jenny Holifield (Kootenai NF), Bill Kranland, and 



Coburn Currier, Bryce Maxell, and Susan Lenard 
(MTNHP); on the Flathead by Nate Schwab 
(USFS), Lewis Young, Pat Shanley, and Jenny 
Holifield (Kootenai NF); on the Helena NF by 
the 2006 Beartooth WMA Bat training crew, Nate 
Schwab (USFS), Pat Shanley, and Bryce Maxell 
(MTNHP); on the Lewis and Clark NF by Eric 
Tomasik (Lewis & Clark NF), Coburn Currier and 
Bryce Maxell (MTNHP); on the Kootenai NF by 
Jenny Holifield (Kootenai NF) and Lewis Young; 
and on the Lolo NF by Sarah Kaufman, Karina 
Mahoney, and Bryce Maxell (MTNHP). 

Scott Blum (MTHNP) entered survey data into 
the Montana Natural Heritage Program's Point 
Observation Database, facilitating the production 
of new distribution maps and the updating of 
element occurrence data in the Montana Natural 
Heritage Program's Biotics database. 



Table of Contents 

Introduction 1 

Methods 3 

Grid Cell Identification 3 

Focus of 2006 Efforts 3 

Field Methods 7 

Detection Probability Analysis and Program PRESENCE 7 

Results and Discussion 10 

Overview 10 

Species Captured During Mist-netting and Acoustic Surveys 10 

Naive Detection Rates by Survey Method 12 

Number of Species Detected by Survey Method 13 

Survey Coverage with Sampling Grid 14 

Detection Probability Analysis and Results 16 

Need for a State Bat Grid 19 

Recommendations 20 

Literature Cited 21 

Appendix A. Global/State Rank Definitions 

Appendix B. Distribution Maps for Bats in Montana 

Appendix C. Application of Oregon Bat Grid to Montana - Cell Ownership and Accessibility 

Appendix D. Site Locations for USFS 2006 Bat Surveys 

Appendix E. Documented Species List per Forest/District 

Appendix F. Site Occupancy and Detection Probability Analysis 

List of Tables 

Table 1. Oregon Bat Grid Cell Count for USFS Forests in Montana 3 

Table 2. Model Descriptions 8 

Table 3. Number of Survey Sites per District in 2006 10 

Table 4. Species list for 2006 and Site Survey Detection Method 11 

Table 5. Overall percent detection rate for species during acoustic surveys versus mist- 
netting surveys on eight Region 1 National Forests in Montana, 2 July - 28 

September, 2006 12 

Table 6. Average Number of Species per Detection Method 13 

Table 7. Total Number of Cells per Forest with Multiple Surveys (Fit Protocol) for 

2006 and combined 2005 & 2006 as Percentage of Overall Total Cells 14 

Table 8. Comparison of All Montana Bat Data (MTHNP Point Observation Database) 
and data collected in Multiple Survey Cells in 2006 with Overall Predicted 

Number of Species 16 

Table 9. Bat Detection Probability Summary 17 



VI 



List of Figures 

Figure 1. Montana Bat Sampling Grid 4 

Figure 2. Land Status and Accessibility of Oregon Bat Grid Applied to Montana 5 

Figure 3. 10km x 10km Grid Overlay with 2006 USFS Survey Locations 6 

Figure 4. Grid Cell Survey Status 15 



vn 



Introduction 



Recognition of a general lack of basic natural 
history information on native bat species (Hayes 
2003), widespread disturbance, alteration, and/or 
complete removal (Fenton 1997, Pierson 1998) 
of habitats traditionally used by bats for roosting 
and foraging have contributed to increasing 
concern in recent decades about the status of 
bats throughout North America. As a result, six 
species or subspecies of bats in the continental 
United States are currently classified as endangered 
under the United States Endangered Species Act 
of 1973 (O'Shea et al. 2003). While none of these 
federally listed bats occur in Montana, six other 
species are recognized by the state as Species of 
Concern (Eastern Red Bat - Lasiurus borealis (G5 
S2S3); Fringed Myotis - Myotis thysanodes (G4G5 
S3); Northern Myotis - Myotis septentrionalis 
(G4 S2S3); Pallid Bat - Antrozous pallidas (G5 
S2); Spotted Bat - Euderma maculatum (G4 
S2); Townsend's Big-eared Bat - Corynorhinus 
townsendii) (G4 S2) (See Appendix A for Rank 
Definitions) (MTNHP and MTFWP 2006). 

While conservation and protection of roosts are 
important long-term management considerations 
for many North American bat species (Sheffield 
et al. 1992), efforts to conserve bats in Montana 
are often hampered by a lack of data on general 
habitat requirements. For example, the little data 
available from Montana on foraging behavior and 
diet of bats have largely been obtained at water 
sources (Jones et al. 1973), with no knowledge of 
where the foraging bats are roosting. Conversely, 
studies of bat roosts in Montana (e.g., Worthington 
1991a, 1991b, Hendricks et al. 2000, 2004) lack 
information on where and how far the roost 
members go to feed and drink. Additionally, 
patterns of roost selection and fidelity (e.g., 
Sherwin et al. 2003) have not been studied in 
Montana, even though it is understood that suitable 
summer and winter roosts may limit the local 
and regional distribution and abundance of many 
temperate-zone bats (Humphrey 1975, Dobkin et 
al. 1995), especially cave- and crevice -dwelling 
taxa. 



Most bat species use a variety of localized 
habitats for roosting, whether natural sites (e.g., 
caves, trees, rock crevices) or man-made sites 
(e.g., buildings, mines, bridges). Sites may be 
used only for specific purposes during specific 
seasons of the year. Recent research on bat roosts 
in Montana has followed the national pattern 
of inventorying and monitoring roosts in caves, 
abandoned mines, and bridges (e.g., Worthington 
1991a, 1991b, Hendricks et al. 2000, 2004, 2005; 
Hendricks and Kampwerth 2001), and remains an 
important activity for a state bat conservation plan. 
Nevertheless, sampling bats across the landscape 
at foraging sites continues to be critical for filling 
gaps in documented distribution, assessing relative 
abundance of local populations, and ultimately 
identifying roost locations. 

Efforts over the past two years have improved 
understanding of the distribution and status of bats 
on US Forest Service Northern Region lands in 
Montana. The effort has generally followed the 
Oregon Bat Grid Protocols designed to inventory 
the presence of bat species using a standardized 
effort and sample unit (a 10 x 10 km 2 grid) across 
the state. The protocol consists of collecting 
baseline data on acoustic, morphologic, and genetic 
characteristics for bats species in the Region. 
While important information has been gathered 
on Montana's bats more work needs to be done to 
continue filling in distribution holes and identifying 
important roosting locations. A summary of all 
existing bat records across the region clearly shows 
large distribution gaps for all species, further 
underscoring the need for additional surveys (see 
Appendix B). In particular, large portions of the 
Beaverhead-Deerlodge NF, Custer NF, Flathead 
NF, Gallatin NF, and Lewis and Clark NF still 
lack records for any bat species. Insufficient 
data may affect bat populations and the habitat 
they use for roosting and foraging because of 
potential unintended consequences from a variety 
of management activities. The Northern Region 
recognized the need for additional documentation 
of bats on Forest Service lands to address inventory 



and monitoring requirements, and initiated bat 
surveys in 2005 across the Region on selected 
National Forest Ranger Districts. Given the large 
areas of the Region lacking bat data, a second year 
of surveys, generally following the 2005 protocols, 
was conducted in 2006 to fill in data gaps. 

The primary objective of the 2006 survey was to 
document bat species richness (number of species) 
within sample units for areas with limited or no 
bat data. The longer-term objective was to infer 
sample unit occupancy for each species across 
entire Ranger Districts by implementing a grid- 
based sampling methodology. 

While our primary goals for the 2006 field season 
were to fill in data gaps for as many bat species as 
possible, we also completed some ground work for 
future inventory, monitoring, and predictive habitat 
modeling. We evaluated detection probabilities 
for bats at 33 different grid survey cells from 
2005 and 2006 throughout the USFS Region 1 
Forests in Montana. This was done in order to: (1) 
compare naive site occupancy rates with robust 
estimates of site occupancy that correct for the 



fact that species are not always detected at all sites 
where they are present; and (2) take steps to model 
species' occupancy rates in different habitats while 
simultaneously addressing the issue that detection 
probabilities may vary by a variety of site (e.g., 
elevation and temperature) and sampling (e.g., 
duration of survey and survey type - acoustic or 
mist-netting) covariates. Explicitly addressing the 
fact that species are detected imperfectly in the 
context of various site and sampling covariates 
is important in order to ensure that: (1) species 
that appear to be rare from naive estimates of site 
occupancy resulting from single surveys of sites 
truly are rare; (2) managers have a sound basis 
for making management decisions with regard 
to the status of species in various habitats and 
across various portions of the species' range where 
their status may be quite different; (3) monitoring 
programs are adequately designed (i.e. enough 
visits of enough sites) to detect biologically 
meaningful changes in the occupancy rates of 
different habitats; and (4) predictive distribution 
models account for variable rates of occupancy of 
different habitats. 



Methods 



Grid Cell Identification 

One of the first steps in applying the Oregon 
bat grid for USFS Region 1 was to identify cell 
ownership and the associated effort required to 
sample the sites. Cell ownership of the 3983 cells 
covering the state was identified by assigning cells 
to specific landowners if the entity occupied 50 
percent or more of the land area in a cell. Of the 
3983 cells overlaying the state, a total of 821 cells 
covered the Region 1 lands in Montana based on 
this specific criterion. Seven hundred eighty-two 
cells were further assigned to specific individual 
Forests within the state (see Table 1 and Figure 1); 
cells shared between Forests and/or between RDs 
and other public lands were given separate status 
and can be found in Appendix C. The remaining 
cells were assigned to other federal agencies, tribal, 
or private landowners. 

All cells were then categorized as "road accessible"' 
or "road inaccessible" by visual evaluation of the 
extent of existing roads within each cell using 
topographic layers and aerial images (see Figure 
2). As with all survey efforts, on-the-ground 
assessment to determine overall accessibility needs 
to be made at the time of survey. Five hundred 
eighty five of the Region 1 cells were estimated to 
be road accessible; the remaining 197 cells were 
identified as inaccessible by roads and would need 
different logistical effort (see Table 1). Evaluating 
accessibility of cells by roads was necessary to 

Table 1. Oregon Bat Grid Cell Count for USFS Forests in 
Montana 



Forest 


Grid Cell 
Count 


Cells Road 
Accessible 


Beaverhead- 
Deerlodge 


151 


128 


Bitterroot 


60 


41 


Custer 


50 


37 


Flathead 


104 


69 


Gallatin 


86 


39 


Helena 


43 


36 


Kootenai 


114 


110 


Lewis and Clark 


79 


41 


Lolo 


95 


84 


TOTAL 


782 


585 



highlight the logistical differences required to 
sample cells with and without roads. 

As in 2005, the areas selected for survey during 
2006 followed the framework of the Oregon Bat 
Grid from which random-selected grid cells in 
Region 1 were drawn. Using ArcGIS 9.2 the grid 
of square blocks, each 10 Ian on a side (100 km 2 in 
area), was overlaid on each RD to create a target 
population of sampling units (grid cells) to which 
inferred occupancy rates could be made. In order 
to fill in data gaps, general geographic areas with 
limited or no existing bat data were identified for 
survey in 2006. Each qualifying cell within these 
regions was randomly selected using randomly 
generated numbers. Sample units were selected 
from those with the lowest random numbers with 
reasonable access to potential survey sites. 

Focus of 2006 Efforts 

Surveys for bats in Montana were conducted during 
summer (primarily early July to late August) 2006 
on Ranger Districts (RD) in each of eight National 
Forests (NF) of the Northern Region: Beaverhead- 
Deerlodge NF-Butte and Dillon RD, Bitterroot NF- 
Sula and West Fork RD, Custer NF-Ashland and 
Beartooth RD, Flathead NF-Tally Lake RD, Helena 
NF-Helena, Lincoln, and Townsend RD, Kootenai 
NF-Fortine and Rexford RD, Lewis & Clark NF- 
Mussellshell RD and the Lolo NF-Superior RD. 
The Flathead, Kootenai, Lolo, Bitterroot, and one 
of the Helena NF RDs (Lincoln) sampled are west 
of the Continental Divide. The remaining sampled 
RDs are in the central and south central portions of 
Montana east of the Continental Divide. Survey 
sites spanned a range of elevations: 2980-6251 ft 
west of the Divide and 3960-8307 ft east of the 
Divide. The number of sample units surveyed 
differed among Forests as did the number of survey 
nights per cell. For the 2006 Northern Region 1 
inventory, 75 sites were surveyed across 16 RDs 
(Figure 3 and Appendix D). 






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Field Methods 

After the cells in each Forest were selected for 
survey, specific site locations for mist-netting 
and acoustic survey were determined in the field 
by survey crews, sometimes using information 
provided by Forest Service personnel. Sites 
usually contained features that might concentrate 
bat activity; primarily water sources such as ponds 
and streams, less often bridges over streams, caves 
and mines, and least often at or near abandoned 
buildings. Bats were captured using mist nets of 
various lengths and configurations; the number of 
nets deployed varied from site to site. Nets were 
deployed at twilight and left open for at least 3.5 
hours, weather permitting, or until one hour passed 
with no acoustic detections. 

Species physical identification was based on 
published keys and species accounts (van Zyll de 
Jong 1985, Nagorsen and Brigham 1993, Adams 
2003). Standard measurements (weight, forearm 
length, ear length) and sex, age, and reproductive 
status were obtained for each individual. Wing 
punch tissue samples were also collected from each 
captured bat until five punches per species were 
accumulated from each site. Tissue was taken 
using sterile procedures and stored in biopsy tubes 
containing desiccant and/or ethanol. Tissues are to 
be used for genetic identification of species pairs 
difficult to distinguish in the field (especially Little 
Brown Myotis - Myotis lucifugus versus Yuma 
My otis - M. yumanensis); genetic analysis was 
initiated before the writing of this report but was 
not completed. 

The survey protocol also called for acoustic 
monitoring at each site using a Pettersson D-240x 
detector and an MP3 recording device. Acoustic 
surveys were conducted either by hand or by 
remote recording; remote recordings off-site from 
the netting location by at least one kilometer were 
counted as separate surveys. Recorded calls were 
subsequently analyzed using Sonobat software and, 
primarily, an unpublished bat species identification 
key provided during the 2006 training session 
(Szewczak, personal communication, July 2006). 
Calls collected by the Montana Natural Heritage 
Program were identified by Heritage Program 
staff. Call data collected by USFS survey teams 



is currently being analyzed; analysis was not 
completed before the writing of this report. 

Data was recorded on standardized data sheets, and 
later transcribed to a Point Observation Database 
housed at the Montana Natural Heritage Program, 
Helena where it is available for agency and public 
use. 

Detection Probability Analysis 
and Program PRESENCE 

We used program PRESENCE (Mackenzie et al. 
2002, 2005) to compare the fit of a priori developed 
candidate models to the pilot bat detection data. 
The specific goals of the modeling effort were 
to: (1) estimate detection probabilities (p) for 
individual species; (2) identify the extent to which 
detection probabilities differ between survey 
types; (3) identify the extent to which detection 
probabilities differ between survey duration; (4) 
compare estimated site occupancy rates (Psi) to 
the naive percentage of sites where species were 
detected; and (5) use estimates of (p) to identify the 
number of sites needed and number of surveys per 
site needed to achieve various confidence intervals 
for estimates of site occupancy in future inventory 
and monitoring efforts. 

It is worth noting the assumptions associated with 
this modeling effort using program PRESENCE 
(Mackenzie et al. 2005) and the extent to which 
these assumptions may have been violated. Key 
assumptions and the degree to which they were 
likely violated include: 

(1) Sampled patches are representative of 
unsampled patches so that inferences can 
be correctly made to the entire population 
of interest. Water bodies were targeted 
across all sites where the pilot detection 
probability surveys were performed so 
inferences are probably limited to areas 
near water. 

(2) Species do not emigrate from or immigrate 
to the sample units between surveys (also 
known as the closure assumption). This 
assumption is clearly violated as our 
surveys occurred across two years. It is 



possible the occupancy rates were different 
between years. For this analysis we 
assume these rates to be constant, this may 
or may not be true. 

(3) Surveys are independent of one another 
(e.g., detections at one site do not depend 
on the detections at another site). There 
is no evidence the presence of mist-net 
or acoustic stations at one location affect 
detections at mist-net or acoustic stations 
at other locations so, the assumption of 
independent surveys does not appear to 
have been violated. 

(4) Species are correctly identified so that 
there are no false detections. Species 
and/or calls not definitively identified 
were not included in the analysis and were 
essentially treated as non-detections so this 
assumption does not appear to have been 
violated. 

(5) All sources of heterogeneity are modeled. 

This assumption is almost certainly 



violated because a number of site (e.g., 
elevation) or sampling (e.g., start and 
end temperature) covariates were not 
incorporated into the candidate models. 
However, we do not consider this violation 
to be important in the context of the 
specific goals of this analysis. That is, we 
were largely focused on understanding 
approximate site occupancy and detection 
rates, difference between naive site 
occupancy rates and estimates involving 
correction for detection probability, 
and planning for future inventory and 
monitoring efforts, not specific questions 
about how individual species respond to 
differences in habitat or habitat conditions. 

A set of 8 simple a priori candidate models was 
developed in order to address these questions. 
More complex models were not considered because 
the limited pilot data gathered was not suitable for 
estimating large numbers of parameters. 



Table 2. Model Descriptions 



Model Notation 


Model Description 


Psi(.),p(.) 


Site occupancy rate (Psi) is constant across all sites surveyed. Detection 
probability (p) is constant across all surveys. 


Psi (.), p(s) 


Site occupancy rate (Psi) is constant across all sites surveyed. Detection 
probability (p) varies by individual survey. 


Psi (.), p(type) 


Site occupancy rate (Psi) is constant across all sites surveyed. Detection 
probability (p) varies by survey type. 


Psi (.), p(duration) 


Site occupancy rate (Psi) is constant across all sites surveyed. Detection 
probability (p) varies by survey duration. 


Psi (.), p(s*type) 


Site occupancy rate (Psi) is constant across all sites surveyed. Detection 
probability (p) varies by individual survey and survey type. 


Psi (.), p(s*duration) 


Site occupancy rate (Psi) is constant across all sites surveyed. Detection 
probability (p) varies by individual survey and survey duration. 


Psi (.), p(type*duration) 


Site occupancy rate (Psi) is constant across all sites surveyed. Detection 
probability (p) varies by survey type and survey duration. 


Psi (.), 
p(s*type*duration) 


Site occupancy rate (Psi) is constant across all sites surveyed. Detection 
probability (p) varies by individual survey, survey type, and survey 
duration. 



Relative fit of the a priori models to the data was 
evaluated using Akaike Information Criteria (AIC) 
which balances the fit of the model to the data with 
a penalty for the number of parameters used in the 
model in order to arrive at the most parsimonious 
model (Burnham and Anderson 2002). The best 
fitting model has the lowest AIC value and models 
within 2 AIC values of one another essential have 
the same level of support in terms of how well the)' 
describe the data given the number of parameters 
involved. 

The Simulations module in program PRESENCE 
was used to examine different scenarios for future 
inventory and monitoring efforts. For these 
analyses, the true proportion of sites occupied 
was varied in order to encompass the range of 
site occupancy rates (0.3, 0.5, 0.7, and 0.9) and 
detection probabilities (0.2, 0.4, 0.6, and 0.8) 
observed during the pilot study and likely to be 
encountered with bat species in other regions of 



Montana. For each combination of site occupancy 
rate and detection probability three major levels 
of survey effort and/or funding were considered; 

(1) 100 sampling days = 400 site surveys which 
is approximately equivalent to twice the level of 
effort made during the 2005 and 2006 field surveys, 

(2) 50 sampling days = 200 site surveys which 
is approximately equivalent to the level of effort 
made during the 2005 and 2006 field surveys, and 

(3) 25 sampling days =100 site surveys which 
is approximately equal to half the level of effort 
made during the 2005 and 2006 field survey. A 
number of scenarios were considered for each 
level of survey effort in which the number of sites 
surveyed multiple times (M), the number of times 
those multiple survey sites where surveyed (S), and 
the number of roost sites surveyed a single time 
(Roost) were varied in order to examine the effect 
different allocations of the same level of effort had 
on the standard error (SE) of the estimate of the site 
occupancy rate (Psi). 



Results and Discussion 
Overview 

The summer 2006 survey helped fill a number 
of distribution gaps, highlighted the importance 
of including acoustic sampling in bat survey 
efforts, and produced several new county records. 
In addition, new locations were recorded for 
the Spotted Bat, a Region 1 Sensitive Species, 
including one on the Helena RD of the Helena NF 
which represents a westward range extension in the 
state of approximately 260 km. Limited success at 
capturing other USFS Region 1 Sensitive Species 
and State Species of Concern (see Appendix 
A) suggests the need for specific methodology 
targeting these species. The only other Species of 
Concern recorded during the 2006 survey efforts 
was the Fringed Myotis which was detected at nine 
of the 75 sites (six sites through acoustics and three 
sites by mist-net). 

Species Captured During Mist- 
Netting and Acoustic Surveys 

Seventy-five sites were sampled for bats across the 
eight USFS Northern Region 1 Forests in Montana 
in 2006 (see Table 3). The Custer NF and Lewis 
and Clark NF had the greatest number of surveys, 

Table 3. Number of Survey Sites per District in 2006 



19 and 11, respectively. Thirty-two sites were west 
of the Continental Divide, while 43 sites were east 
of the Divide (Figure 3). Bats were detected at 71 
of the 75 sites (see Appendix D for site locations 
and species detected at each location). 

Ten bat species were recorded during acoustic 
surveys and nine during mist-netting efforts (Table 
4). Nine species were captured at sites west and 
ten species at sites east of the Continental Divide. 
The Spotted Bat was the only species encountered 
during the 2006 surveys not detected west of 
the Divide and it was documented by acoustic 
recording only. 

The summer 2006 Northern Region survey resulted 
in new county records for nine species (see maps 
in Appendix B): Big Brown Bat (Eptesicus fuscus) 
(Mineral and Meagher), Hoary Bat (Golden Valley 
and Meagher), Spotted Bat (Lewis and Clark), 
Western Small-footed Myotis (Myotis ciliolabrum) 
(Stillwater), Western Long-eared Myotis 
(Stillwater), Fringed Myotis (Beaverhead, Powell, 
and Stillwater), Long-legged Myotis (Meagher 
and Powell), and Little Brown Myotis (Stillwater). 
With the addition of four species, Stillwater County 
(Custer NF) received the most new records. 



Forest 


Ranger Districts 
surveyed in 2006 


Number of survey 
sites 2006 


Beaverhead-Deerlodge 


Butte 


4 




Dillon 


5 


Bitterroot 


Sula 


5 




West Fork 


1 


Custer 


Ashland 


1 




Beartooth 


19 




Sioux 


1 


Flathead 


Tally Lake 


7 




Rexford 


1 


Helena 


Helena 


1 




Lincoln 


3 




Townsend 


1 


Kootenai 


Fortine 


7 


Lewis and Clark 


Mussellshell 


11 


Lolo 


Superior 


8 


Total 




75 



10 



Table 4. Species list for 2006 and Site Survey Detection Method 



Species List for 2006 sites 


Number of Surveys 

where Species was 

Detected 


Total # of 
sites where 
Species was 

Detected 


Acoustic 


Mist-net 


Spotted Bat (Euderma maculatum) 








o 

J 


Big Brown Bat {Eptesicus fuscus) 


13 


12 


22 


Hoary Bat {Lasiurus cinereus) 


28 


13 


35 


Silver-haired Bat (Lasionycteris noctivagans) 


12 


20 


27 


California Myotis {Myotis californicus) 


3* 


5 


8 


Western Small-footed Myotis (M. ciliolabrum) 


13 


5 


16 


Long-eared Myotis (M. evotis) 


23 


20 


36 


Little Brown Myotis (M. lucifugus) 


30 


9 


33 


Fringed Myotis (M. thysanodes) 


6 





9 


Long-legged Myotis (M. volans) 


7 


20 


25 



* all acoustic data show characteristics definitive to Myotis californicus, yet these observations remain 
tentative due to lack of in-hand evidence of the species in these regions which are quite distant from 
previously documented localities for the species. 



The Spotted Bat detection was the first detection 
of a Spotted Bat during the USFS survey efforts. 
Two species captured in 2005, the Townsend's Big- 
eared Bat and Yuma Myotis, were not observed 
in 2006. No Townsend's Big-eared Bats were 
documented acoustically or by mist-net during the 
2006 survey efforts. Tentative identification was 
made for Yuma Myotis at mist-netting sites, but 
no acoustic recordings confirmed their presence 
and no genetic analysis has been performed to 
confirm the presence of this species in the state. 
The presence of this species in Montana, therefore, 
is highly questionable given the lack of definitive 
documentation through genetic data from tissue 
samples or acoustic data (Montana Bat Working 
Group, annual meeting, February 2007). All 
previously recognized observations of Yuma 
Myotis appear to be misidentifications of Little 
Brown Myotis given recent acoustic analysis at a 
number of roost sites. No Townsend's Big-eared 
Bats were documented acoustically or by mist-net 
during the 2006 survey efforts. 

Tentative identifications of California Myotis were 
made at three sites across two counties during the 
2006 season. Recordings of call characteristics 



consistent with Myotis californicus were made at 
two locations in Beaverhead County approximately 
70 km east of previously documented California 
Myotis observations (e.g., Ravalli, Missoula and 
Lake Counties [Hendricks and Maxell 2005]). 
A third call series was identified as Myotis 
californicus in the Pryor Mountains in Carbon 
County in 2006 which would represent an eastward 
range expansion of approximately 400 kilometers. 
Interestingly, tentative identification was made for 
a California Myotis during a mist-netting effort 
in the Bighorn Canyon National Recreation Area 
in 2004 (Keinath 2004, 2005) approximately 20 
kilometers east of the 2006 USFS site. Although 
several individuals identified the 2004 specimen 
as Myotis californicus while in hand, without 
genetic analysis the species is considered 
unconfirmed at this location (Doug Keinath, 
personal communication). Szewczak (personal 
communication 2007) confirmed the identification 
of the calls recorded on the Custer National Forest, 
yet agreed they should be considered tentative until 
genetic confirmation of the species in this general 
area has been made. Without in hand evidence 
of California Myotis at the three locations, these 
observations remain tentative. 



11 



Additional bat inventory work conducted in 2006 
by the Montana Natural Heritage Program in 
eastern Montana resulted in numerous additional 
county records for these and other species. Up-to- 
date distribution maps for Montana's species can be 
queried and viewed with a variety of map layers on 
the Montana Natural Heritage Program's Tracker 
website at: http ://mtnhp . or g/Tracker . 

Naive Detection Rates by Survey 
Method 

Sixty three mist-net and 43 acoustic surveys were 
conducted in 2006 (32 sites were mist-net-only 
surveys, 12 sites were acoustic-only surveys, and 
31 sites were both). With the exception of the 
Spotted Bat acoustic detection, all species were 
recorded using both survey methods. However, 
the percent of sites at which species were detected 
varied between the two survey methods (see 
Table 5). Acoustic surveys outperformed mist-net 
surveys in the number of species documented per 



site and overall naive estimates of site occupancy. 
The average detection rate for acoustic sampling 
was 38.2% (range = 8.3 to 83.3%; median = 
34.7%), while the average naive detection rate for 
mist-netting was 18.0% (range = 0.0 to 33.3%; 
median = 18.4%). These results are supported 
by the best-fitting candidate models which 
showed that acoustic sampling boosted detection 
probabilities. The most abundant species as 
determined by the acoustic sampling was the Little 
Brown Myotis, which was detected at 83.3% of 
the acoustic surveys. However, this species was 
only detected at 15.0% of the mist-net sites. The 
Hoary Bat (Lasiurus cinereus) was the second most 
abundant species detected acoustically (77. 8% 
of sites), but were only detected at 21.7% of the 
mist-net stations. Based on the analyzed call data, 
the Long-legged Myotis {Myotis volans) was the 
only species detected more frequently during mist- 
net surveys than acoustic surveys (33.3% versus 
19.4%). Two other species, Silver-haired Bat 
(Lasionycteris noctivagans) and Western Long- 



Table 5. Overall percent detection rate for species during acoustic surveys versus mist-netting surveys on eight Region 
1 National Forests in Montana, 2 July - 28 September, 2006. Forty-three acoustic surveys and 63 mist-netting surveys 
were conducted across 75 sites. State Species of Concern are in bold. 



Species 


Overall Percent Detection Rate 


Acoustic 
n=36 a 


Mist-net 
n=60 b 


Little Brown Myotis {Myotis lucifugus) 


83.3 


15.0 


Western Long-eared Myotis (Myotis evotis) 


63.9 




Fringed Myotis {Myotis thysanodes) 


16.7 


5.0 


Long-legged Myotis (Myotis volans) 


19.4 




California Myotis (Myotis californicus) 


8.3* 


8.3 


Western Small-footed Myotis (Myotis ciliolabrum) 


36.1 


8.3 


Silver-haired Bat (Lasionycteris noctivagans) 


33. J 


*5 o o 


Big Brown Bat (Eptesicus fuscus) 


36.1 


21.7 


Hoary Bat (Lasiurus cinereus) 


77.8 


21.7 


Spotted Bat (Euderma maculatum) 


8.3 


0.0 



" analysis is not complete for all acoustic surveys 

b three mist- netting locations resulted in capture of single individuals needing genetic analysis for identification. 

*the presence of this species at three survey sites is in question although the calls for Carbon County were verified by J. 

Szewczak (personal communication 2007). This record would represent a significant eastward expansion of previously 

documented range in Montana. While an individual was documented in-hand east of this location during a 2004 

unrelated study, genetic analysis needs to be performed to confirm its identification. 



12 



eared Myotis (M. evotis), shared the same overall 
mist-net detection rate (33.3%). The Silver-haired 
Bat was detected at the same rate on the acoustic 
surveys (33.3%) while the Western Long-eared 
Myotis was detected at nearly twice the rate during 
the acoustic surveys (63.9%). Acoustic data for 
seven site locations has yet to be analyzed so 
acoustic detection rates will increase for some 
species when the analysis is completed. 

Number of Species Detected by 
Survey Method 

The detection success rate of acoustic and mist- 
net surveys, measured as the average number of 
species detected, differed among all sites pooled, 
as well as sites where both survey methods were 
employed. Acoustic surveys only produced 
an average of 4.44 species per site. Sites with 
combined acoustic and mist-net surveys resulted 
in an average of 3.96 species per site. The average 
number of species recorded during mist-net surveys 
alone was only 2.03 species (see Table 6). Failure 
to detect any species occurred only at those sites 
where mist-net surveys were the only survey 
method employed (four sites). 

Acoustic surveying has great potential to provide 
rapid assessment of species distributions over 
many sites (Hayes 1997, O'Farrell and Gannon 
1999) as well as to identify areas of significant 
concentrations of species and individuals. 
Remote acoustic monitoring stations also have 
an advantage over traditional capture methods 
by greatly enhancing the number of bat species 



documented in an area while requiring less field 
effort. It is important, however, to have equipment 
available and field crews trained in the use of this 
technology well in advance of field surveys. Even 
with a training session designed to familiarize 
attendees with the technology, slightly more mist- 
netting surveys (32) occurred without acoustic 
sampling in 2006 than those with (3 1), suggesting 
that some field personnel were not comfortable 
employing the acoustic sampling methods. While 
mist-nets have been used as the traditional method 
for documenting bat species in an area, mist-net 
surveys alone probably under-represent total bat 
species richness in a sample unit more often than 
not. With an increasing ability to identify calls to 
species level, acoustic sampling can be used, under 
some circumstances, not only to augment mist- 
netting efforts, but as a primary data-gathering tool. 

We consider acoustic surveys an integral 
component of future inventory and monitoring 
schemes to be used to augment more traditional 
capture methods. The Montana Natural Heritage 
Program has begun building a collection of 
calls for bats recorded in Montana. This is the 
first step in building a library of reference calls 
from individuals within the state whose identity 
is definitive through morphologic and genetic 
measurements. The three sets of data (acoustic, 
morphologic, genetic) will provide future workers 
using acoustic monitoring the reference tools 
needed to identify and account for regional 
differences in calls. 



Table 6. Average Number of Species per Detection Method 



Capture Method 


# of sites 


Average # of 
Species 


Standard 
Deviation 


Acoustic only 


9* 


4.44 


2.74 


Acoustic and mist-net 


27** 


3.96 


1.76 


Mist-net only 


36 


2.03 


1.48 



* this data is based upon only 9 of the 12 acoustic only sites. Call data for 3 of 
the sites has not yet been analyzed. 

** Thirty-one sites were sampled by both acoustic and mist-netting techniques. 
Call data for 4 sites has not been analyzed; these sites were included in the 
mist-net only analysis. 



13 



Survey Coverage with Sampling 
Grid 

Multiple surveys were conducted in 16 different 
grid cells (See Figure 4) in 2006, representing 37 
of the 75 sites surveyed that year. These fit the 
protocol requirement designed for Montana of at 
least two surveys per sample unit (see Table 7) 
(Montana Bat Grid Draft Protocol, unpublished 
document, 2006). Combined with data from 2005, 
33 cells fit the protocol requirement and were 
surveyed at two or more locations per cell. The 
Forest with the greatest overall cell coverage (as 
a percent of the total number) is the Lewis and 
Clark at 8%, followed by the Helena (7%) and the 
Flathead (6%). All other Forests have had 4% or 
fewer cells surveyed using the Montana Bat Grid 
Protocol. 

One of the requirements of the bat grid protocols 
involves identifying species predicted to occur 
in the grid cells based upon existing information 
on the general distributions of species. The 
success rate, i.e. the percentage of species detected 
compared to the predicted species for that location, 
for 2005 and 2006 ranged from 27% to 92% (see 
Table 8). 

While the predicted species lists are generated 
from general distribution maps, the data from the 
Heritage Point Observation Database indicates 
that the full compliment of predicted species has 



been documented on only two Districts, Beartooth 
RD-Custer NF and Libby RD-Kootenai NF While 
it might be anticipated that not all species will 
ultimately be documented where predicted, the 
limited success rates for 2005 and 2006 suggests 
much greater effort needs to be employed to 
adequately survey all Districts for bat species. 
Only when species are documented by field 
surveys will we gain better understanding of their 
distribution and habitat needs rather than relying 
solely on predicted presence (see Appendix E for a 
list of documented bat species per Region 1 USFS 
Districts). 

As in 2005, the 2006 survey efforts focused 
on and identified numerous areas where bats 
concentrate their activity while seeking food and 
water resources. Some of these sites, especially 
those used by several bat species, may be useful 
in the future for monitoring efforts across Forest 
Districts. While these sites could be used to 
develop a comprehensive survey and monitoring 
scheme, both for the Northern Region and all 
of Montana, one of the important next steps 
is to adopt a sampling grid that is both easily 
implemented and broadly applicable. While the 
Oregon Bat Grid can be useful and provides a 
uniform basis from which sampling sites can be 
selected, the application of this grid is somewhat 
cumbersome. The grid's orientation is skewed 
(trending northwest to southeast) and follows no 
standard lines of orientation. While the uniform 



Table 7. Total Number of Cells per Forest with Multiple Surveys (Fit Protocol) for 2006 and combined 2005 & 2006 as 
Percentage of Overall Total Cells. 



Forest 


Cell Count 


Fit 

Protocol 

2006 


Fit Protocol 

for years 
2005 & 2006 


Percent of "Protocol 

cells" surveyed in 

Forest 


Beaverhead-Deerlodge 


151 


4 


4 


•20/ 

J /O 


Bitterroot 


60 


2 


2 


3% 


Custer 


50 


2 


2 


4% 


Gallatin 


86 





1 


1% 


Flathead 


104 


2 


6 


6% 


Helena 


43 





o 
j 


7% 


Kootenai 


114 


1 


5 


4% 


Lewis and Clark 


79 


4 


6 


8% 


Lolo 


95 


1 


1 


1% 


TOTAL 


782 


16 


30 


4% 



14 



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u 


u 


- 






C 


£ 


- 


il 




a 


a 


-5 


u 




bo 


BO 


ve 


c 




v 


i. 


o 












ft. 


c 




3 




*f 


*^ 


09 


3 




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a 






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0> 


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s. 


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ua 




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i< 


= 


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15 



Table 8. Comparison of All Montana Bat Data (MTHNP Point Observation Database) and data collected in Multiple 
Survey Cells in 2006 with Overall Predicted Number of Species. 


Forest 


District 


Predicted 

Number of 

Species 


Point 

Observation 

Database 

as % of 

Predicted 


2005 & 2006 
Protocol 

Data as % of 
Predicted 


Beaverhead/Deerlodge 


Butte 


10 


70% 


60% 




Dillon 


10 


80% 


60% 


Bitterroot 


Sula 


11 


45% 


36% 


Custer 


Beartooth 


12 


100% 


92% 


Flathead 


Swan Lake 


11 


45% 


45% 




Tally Lake 


11 


55% 


55% 


Gallatin 


Bozeman 


10 


70% 


60% 


Helena 


Helena 


11 


82% 


64% 




Lincoln 


11 


55% 


27% 




Lownsend 


11 


91% 


91% 


Kootenai 


Fortine 


11 


73% 


27% 




Libby 


11 


100% 


73% 


Lewis and Clark 


Judith 


10 


70% 


70% 




Mussellshell 


10 


70% 


70% 


Lolo 


Superior 


11 


82% 


55% 



grid cell size (10x10 km 2 ) may be desirable, 
identifying one's location on the ground, or its 
associated identifying label is impossible without 
a GIS grid overlay in hand. This makes field 
organization and navigation somewhat problematic, 
especially when the number of surveys conducted 
per cell, as described in the draft Oregon protocols, 
is important. 

A more useful sampling scheme for a broad-scale 
bat inventory would converge with the Oregon 
Bat Grid which incorporates elements of a typical 
state bird atlas to help guide sampling efforts 
to each sample unit. In the Oregon scheme, the 
primary objective is to document all species on a 
list of expected species generated for each sample 
unit. Each sample unit is surveyed using multiple 
detection methods, as we attempted to do in 2005 
and 2006, but also is visited as many times (up to 
12) as it takes to achieve the species richness goal, 
rather than limiting the survey effort to two or 
fewer visits, as was done in 2005 and 2006. Even 
for roost monitoring of a species like Townsend's 
Big-eared Bat, there is so much detection 
variability during any single visit (due to a variety 



of site and sampling covariates) that as many as 
nine visits to a site may be necessary to identify a 
non-roost (Sherwin et al. 2003). Although the 2006 
survey helped to further fill in distribution gaps and 
generated much useful data, limited human and 
monetary resources kept the survey from achieving 
the objective of determining species richness for 
most sample units visited, largely because too few 
site visits were made. This failure greatly limits or 
prohibits the ability to infer sample unit occupancy 
across Districts. 

Detection Probability Analysis 
and Results 

Data for estimating site occupancy rates and 
detection probabilities was gathered for 8 of the 
10 species detected during the 2005-2006 USFS 
Region 1 bat surveys (Table 9). Two species 
detected during these surveys had insufficient data 
for estimates: California Myotis is typically of 
limited range and unlikely to be encountered across 
all Forests and Spotted Bat is presumed a relatively 
rare species of limited distribution. An additional 
five species known to occur in Montana were 



16 



p = Estimated 
Probability of 
Detection (SE) 


o 
in 

o 

o 
o 
in 

o 


^D 
O 

O 
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in 

o 


OO 

o 
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en 

o 


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oo 

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1^ 

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in 

o 


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o 

o 

CM 

CM 
O 


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o 

o- 

■<*■ 
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Psi = Estimated 

Proportion Sites 

Occupied (SE) 


o 
o 
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o 
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17 



either not encountered or were not encountered 
with enough frequency across the two years to be 
considered for analysis. Therefore, alternative 
methods appear to be justified for detection 
and monitoring species with specific habitat 
requirements, limited distributions, or general rarity 
to the state. 

For those species with sufficient data, estimated 
detection probabilities ranged from a low of 0.252 
to a high of 0.596 with mean = 0.43 1 and median 
= 0.480 (see Table 9). The estimated detection 
probability for the only Species of Concern 
was 0.314 for Myotis thysanodes. Abundant 
species with easily distinguished acoustic call 
characteristics had higher detection probabilities 
(range = 0.474 - 0.596). Improved techniques 
in call identification would likely result in higher 
detection rates. Best fitting models for five of the 
eight species analyzed indicate type of survey was 
important in explaining detection probability (mist- 
net sampling having lower detection probabilities 
than acoustic sampling). Thus, there is strong 
evidence that increasing acoustic sampling efforts 
will improve detection of species in inventory and 
monitoring efforts. 

Estimated site occupancy rates that took probability 
of detection into account were all higher than 
naive percentage of sites where species were 
detected (mean = 0.188, range = 0.080 to 0.606). 
Thus, evaluating detection probability is clearly 
important for identifying the success of field efforts 
and should influence the type of survey methods 
employed, especially when presence/non-detection 
is the goal of the study. 

Simulations of standard error (SE) for site 
occupancy rates (Psi) resulting from a number of 
scenarios for survey effort, detection probability 
(p), number of sites surveyed multiple times (M), 
number of times those multiple survey sites where 
surveyed (S), and number of Roosts surveyed 
a single time (Roost), identified a number of 
combinations that resulted in unacceptable levels 
of precision for confidence intervals (Appendix 
F). We considered acceptable confidence interval 
widths to have a maximum SE < 0.097 (i.e., a total 
confidence interval width of 0.388). However, 



even this may not be an acceptable confidence 
interval for evaluating some management or 
status questions. When acceptable confidence 
interval widths were achieved, we highlighted 
scenarios in gray in Appendix F when they allowed 
the greatest number of sites to be surveyed for 
each level of survey effort. In some cases we 
highlighted multiple scenarios associated with the 
same level of survey effort in order to highlight 
tradeoffs that might be faced (e.g., using a smaller 
grid cell size as a sampling unit versus using a 
grid cell comparable to the Oregon bat grid or 
the area covered by a 1:24,000 scale topographic 
map). When no scenarios resulted in acceptable 
confidence intervals under a given level of survey 
effort and Psi and p, then no scenarios were 
highlighted. In general, simulations (Appendix F) 
showed that: 

(1) When site occupancy rates are 
> 0.3, detection probabilities 
need to be > 0.4 before current 
levels of sampling effort result 
in acceptable confidence 
intervals. 

(2) Sampling with approximately 
half of the existing level of 
effort (approximately 25 days 
or 100 surveys) only achieves 
acceptable confidence 
intervals when site occupancy 
rates are > 0.3 and detection 
probabilities are > 0.6. Thus, 
this level of effort would 
certainly not be enough to 
derive confidence intervals 
acceptable for monitoring 
the one Species of Concern 
(Fringed Myotis) for which 
site occupancy and detection 
probabilities were estimated 
in this pilot study and this 

is likely the case for other 
Species of Concern as well. 

(3) While the existing 
level of sampling effort 
(approximately 50 days or 
200 surveys) is adequate for 
monitoring most individual 



species when site occupancy 
rates are > 0.3 and detection 
probabilities are > 0.4, it 
is probably inadequate for 
all Species of Concern. It 
also may be inadequate for 
monitoring larger groups of 
species across larger regions 
because specific habitats/ 
regions of the state may need 
all sampling effort in order to 
achieve the desired confidence 
intervals. 
(4) Doubling the sampling 
effort from existing levels 
(approximately 100 days 
or 400 surveys) allowed 
acceptable confidence 
intervals to be calculated 
with site occupancy as 
low as 0.3 when detection 
probabilities were as low as 
0.2. Furthermore, this level 
of sampling effort allows 
two sets of species with 
non-overlapping ranges in 
at least two different parts 
of Montana to be monitored 
simultaneously as long as 
detection probabilities are at 
least 0.2. 

Need for a State Bat Grid 

While it is beyond the scope of this report to 
explore all the details of what comprises a state 
bat grid, the scheme eventually developed should 
include a hierarchical scale of data collection 
allowing inference of grid cell occupancy rates for 
all species. The objectives of a state bat grid would 
be: 1) to inventory the presence of bat species using 
a standardized survey effort and sampling unit 
across the survey region; 2) collect baseline data on 



acoustic, morphologic, and genetic characteristics 
that serve as reference for bat species identification, 
and; 3) to provide a baseline inventory that 
would allow future monitoring to assess changes 
over time. Inventorying and monitoring bat 
distributions and trends at this scale will place us 
in a better position to address conservation issues 
as they arise. To date, none of these objectives has 
been thoroughly addressed in Montana, although 
the 2005 and 2006 surveys of selected Districts of 
the Northern Region represent an admirable pilot 
effort toward satisfying these objectives. 

We do recommend a bat grid be developed and 
applied to all of Montana. While the Oregon 
Bat Grid offers a scheme from which to design a 
statewide bat grid, we recommend investigating 
the use of the Latilong concept (latitude- and 
longitude -defined polygons). The Latilong concept 
was pioneered by Dr. P.D. Skaar in the late 1960s 
and has been the foundation of wildlife distribution 
applications in the state since then (Lenard et 
al. 2003). While the size of the Latilong blocks 
varies slightly from north to south (blocks at the 
border with Canada are approximately 5% smaller 
than those along the Wyoming border), defining 
the sampling unit to 1:24,000 scale quad maps 
(representing 1/32 of a Latilong block) would 
provide much greater utility in field planning, 
preparations, and protocol execution (easier to 
locate cells on the ground than the Oregon grid). A 
bat sampling grid based upon the Latilong concept 
would also fit well with other current and historical 
wildlife distribution studies in Montana. Although 
we support the Latilong approach, the detection 
probability analysis indicates that there may be 
a need to move to a smaller scale (e.g., Section 
scale). Thus, investigation of a smaller scale grid 
cell should be carefully considered as plans move 
forward for a statewide inventory and monitoring 
effort. 



19 



Recommendations 

1. Emphasize acoustic sampling in all 
future inventory efforts. Greater 
numbers of surveys with higher 
detection rates and total numbers of 
species detected will clearly enhance 
any bat inventory scheme. 

2. Include alternative methods for 
detection of all species (e.g. species- 
specific targeted surveys and specific 
habitat surveys). Low estimates of 
detection probability or insufficient 
data for calculation of estimates were 
associated with a number of rare or 
limited distribution species. 

3. Conduct pilot surveys to evaluate 
baseline levels of site occupancy and 
detection probability for the remainder 
of the bat species in Montana not 



evaluated with this pilot effort. Pilot 
surveys also need to address how 
detection probabilities vary with site 
(e.g., elevation, cover type, forest 
management regime) and sampling 
(e.g., weather, survey duration, survey 
methods) covariates. This pilot survey 
work will place future inventory and 
monitoring efforts on a sound base for 
making management decisions and 
evaluating changes in status 

Use robust estimates of site occupancy 
rates and detection probabilities 
from pilot studies for all species in 
conjunction with budgetary constraints 
to determine the best sampling scheme 
and methodology to address all of 
Montana's bat species. 



20 



Literature Cited 



Adams, R. A. 2003. Bats of the Rocky Mountain 
West. University Press of Colorado, Boulder, 
CO. 289 pp. 

Bumham, K.P. and D.R. Anderson. 2002. Model 
selection and multimodel inference: a practical 
information-theoretic approach. 2 nd Edition. 
New York, New York. Springer- Verlag. 496 p. 

Dobkin, D. S., R D. Gettinger, and M. G. Gerdes. 
1995. Springtime movements, roost use, and 
foraging activity of Townsend's Big-eared Bat 
{Plecotus townsendii) in central Oregon. Great 
Basin Naturalist 55:315-321. 



Hendricks, P., and D. Kampwerth. 2001. Roost 
environments for bats using abandoned mines 
in southwestern Montana: a preliminary 
assessment. Report to the U.S. Bureau of 
Land Management. Montana Natural Heritage 
Program, Helena. 19 pp. 

Hendricks, P., C. Currier, and J. Carlson. 2004. 
Bats of the BLM Billings Field Office in 
south-central Montana, with emphasis on the 
Pryor Mountains. Report to Bureau of Land 
Management Billings Field Office. Montana 
Natural Heritage Program, Helena, MT. 19 pp. 
+ appendices. 



Fenton, M. B. 1997. Science and the conservation 
of bats. Journal of Mammalogy 78:1-14. 

Foresman, K. R. 2001. The wild mammals of 
Montana. American Society of Mammalogists, 
Special Publication No. 12. 278 pp. 

Hayek, L. C. 1994. Analysis of amphibian 
biodiversity data. Pp. 207-269. In: W. R 
Heyer, M. A. Donnelly, R. W. McDiarmid, L. 
C. Hayek, and M. S. Foster (eds). Measuring 
and monitoring biological diversity: standard 
methods for amphibians. Smithsonian 
Institution Press. Washington, D.C. 364 pp. 

Hayes, J. P. 1997. Temporal variation in activity 
of bats and the design of echolocation- 
monitoring studies. Journal of Mammalogy 
78:514-524. 



Hendricks, P., S. Lenard, C. Currier, and J. 
Johnson. 2005. Bat use of highway bridges 
in south-central Montana. Report to Montana 
Department of Transportation. Montana Natural 
Heritage Program, Helena. 3 1 pp. 

Hendricks, P. and B.A. Maxell. 2005. Bat Surveys 
on USFS Northern Region Lands in Montana: 
2005. Report to the USDA Forest Service, 
Northern Region. Montana Natural Heritage 
Program, Helena, MT. 12 pp. plus appendices. 

Hoffmann, R. S., D. L. Pattie, and J. F. Bell. 1969. 
The distribution of some mammals in Montana. 
II. Bats. Journal of Mammalogy 50:737-741. 

Humphrey, S. R. 1975. Nursery roosts and 
community diversity of Nearctic bats. Journal 
of Mammalogy 56:321-346. 



Hayes, J. P. 2003. Habitat ecology and 
conservation of bats in western coniferous 
forests. Pp. 81-119 In Mammal community 
dynamics: management and conservation in the 
coniferous forests of western North America (C. 
J. Zabel and R. G. Anthony, eds.). Cambridge 
University Press, Cambridge, UK. 709 pp. 

Hendricks, P., D. L. Genter, and S. Martinez. 
2000. Bats of Azure Cave and the Little Rocky 
Mountains, Montana. Canadian Field-Naturalist 
114:89-97. 



Jaberg, C, and A. Guisan. 2001. Modelling the 
distribution of bats in relation to landscape 
structure in a temperate mountain environment. 
Journal of Applied Ecology 38:1169-1181. 

Jones, J. K., Jr., R. P. Lampe, C. A. Spenrath, and 
T. H. Kunz. 1973. Notes on the distribution and 
natural history of bats in southeastern Montana. 
Occasional Papers, The Museum, Texas Tech 
University 15:1-12. 



21 



Kalcounis, M. C, K. A. Hobson, R. M. Brigham, 
and K. R. Hecker. 1999. Bat activity in the 
boreal forest: importance of stand type and 
vertical strata. Journal of Mammalogy 80:673- 
682. 

Keinath, D. 2004. Bat and Terrestrial Mammal 
Inventories in the Greater Yellowstone Network: 
A progress report. Wyoming Natural Diversity 
Database, Laramie, WY. 17 pp. 

Keinath, D. 2005. Supplementary Mammal 
Inventory of Bighorn Canyon National 
Recreation Area. Final Report. Wyoming 
Natural Diversity Database, Laramie, WY. 21 
pp. 

Lenard, S., J. Carlson, J. Ellis, C. Jones, and 
C.Tilly. 2003. P.D. Skaar's Montana Bird 
Distribution, 6 th Edition. Montana Audubon, 
Helena, Montana. 144 pp. 

MacKenzie, D.I., J.D. Nichols, G.B. Lachman, S. 
Droege, J.A. Royle and C A. Langtimm. 2002. 
Estimating site occupancy rates when detection 
probabilities are less than one. Ecology 83: 

2248-2255. 

MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. 
Pollock, J.E. Hines, and L.L. Bailey. 2005. 
Occupancy estimation and modeling: inferring 
patterns and dynamics of species occurrence San 
Diego, CA. Elsevier. 344 p. 



O'Farrell, M. J., and W. L. Gannon. 1999. 
A comparison of acoustic versus capture 
techniques for the inventory of bats. Journal of 
Mammalogy 80:24-30. 

Olson, D. H., W. P. Leonard, and R. B. Bury 
(eds). 1997. Sampling amphibians in lentic 
habitats: methods and approaches for the Pacific 
Northwest. Northwest Fauna 4: 1- 134. 

O'Shea, T J., M. A. Bogan, and L. E. Ellison. 
2003. Monitoring trends in bat populations of 
the United States and territories: status of the 
science and recommendations for the future. 
Wildlife Society Bulletin 31:16-29. 

Pierson, E. D. 1998. Tall trees, deep holes, and 
scarred landscapes: conservation biology of 
North American bats. Pp. 309-325, In Bat 
biology and conservation (T. H. Kunz and P. 
A. Racey, eds.). Smithsonian Institution Press, 
Washington, DC. 365 pp. 

Pierson, E. D., Wackenhut M.C., Altenbach J.S., 
Bradley P., Call P., Genter D.L., Harris C.E., 
Keller B.L., Lengus B., Lewis L., Luce B., 
Navo K.W., Perkins J.M., Smith S., and Welch, 
L. 1999. Species conservation assessment 
and strategy for Townsend's Big-eared Bat 
(Corynorhinus townsendii townsendii and 
Corynorhinus townsendii pallescens). Idaho 
Conservation Effort, Idaho Department of Fish 
and Game, Boise, ID. 68 pp. 



Montana Natural Heritage Program and Montana 
Fish, Wildlife, and Parks. 2006. Montana 
Animal Species of Concern. Helena, Montana: 
Montana Natural Heritage Program and 
Montana Department of Fish, Wildlife, and 
Parks. 17 pp. 

Nagorsen, D. W., and R. M. Brigham. 1993. Bats 
of British Columbia. UBC Press. Vancouver, 
BC. 164 pp. 



Sheffield, S. R, J. H. Shaw, G. A. Heidt, and L. 
R. McClenaghan. 1992. Guidelines for the 
protection of bat roosts. Journal of Mammalogy 
73:707-710. 

Sherwin, R. E., W. L. Gannon, and J. S. Altenbach. 
2003. Managing complex systems simply: 
understanding inherent variation in the use of 
roosts by Townsend's Big-eared Bat. Wildlife 
Society Bulletin 31:62-72. 



Nicholson, A. J. 1950. A record of the Spotted Bat 
(Euderma maculatum) for Montana. Journal of 
Mammalogy 31:197. 



Shryer, J. and D. L. Flath. 1980. First record of 
the Pallid Bat (Antrozous pallidas) from Mon- 
tana. Great Basin Naturalist 40: 1 15. 



22 



Swenson, J. E. 1970. Notes on the distribution 
of My otis leibii in eastern Montana. Blue Jay 
28:173-174. 

Swenson, J. E., and J. C. Bent. 1977. The bats 
of Yellowstone County, southcentral Montana. 
Proceedings of the Montana Academy of 
Sciences 37:82-84. 

Swenson, J. E., and G. F. Shanks, Jr. 1979. 
Noteworthy records of bats from northeastern 
Montana. Journal of Mammalogy 60:650-652. 

Thomas, D. W. 1988. The distribution of bats in 
different ages of Douglas-fir forests. Journal of 
Wildlife Management 52:619-626. 

van Zyll de Jong, C. G. 1985. Handbook of 

Canadian mammals. 2. Bats. National Museum 
of Natural Sciences. Ottawa, ON. 212 p. 

Worthington, D. J. 1991a. Abundance and 
distribution of bats in the Pry or Mountains 
of south central Montana and northeastern 
Wyoming. Report for the Bureau of Land 
Management Billings Resource Area and Custer 
National Forest. Montana Natural Heritage 
Program, Helena, MT 23 pp. 

Worthington, D. J. 1991b. Abundance, 

distribution, and sexual segregation of bats in 
the Pry or Mountains of south central Montana. 
Master's Thesis, University of Montana, 
Missoula, MT. 41 pp. 



23 



Appendix A. Global/State Rank Definitions 



Heritage Program Ranks 

The international network of Natural Heritage Programs employs a standardized ranking system to denote 
global (range-wide) and state status. Species are assigned numeric ranks ranging from 1 to 5, reflecting 
the relative degree to which they are "at-risk". Rank definitions are given below. A number of factors are 
considered in assigning ranks — the number, size and distribution of known "occurrences" or popula- 
tions, population trends (if known), habitat sensitivity, and threat. Factors in a species' life history that 
make it especially vulnerable are also considered (e.g., dependence on a specific pollinator). 

Global Rank Definitions (NatureServe 2003) 

Gl Critically imperiled because of extreme rarity and/or other factors making it highly 

vulnerable to extinction 
G2 Imperiled because of rarity and/or other factors making it vulnerable to extinction 

G3 Vulnerable because of rarity or restricted range and/or other factors, even though it may 

be abundant at some of its locations 
G4 Apparently secure, though it may be quite rare in parts of its range, especially at the 

periphery 
G5 Demonstrably secure, though it may be quite rare in parts of its range, especially at the 

periphery 
Tl-5 Infraspecific Taxon (trinomial) — The status of infraspecific taxa (subspecies or 

varieties) are indicated by a "T-rank" following the species' global rank 

State Rank Definitions 

51 At high risk because of extremely limited and potentially declining numbers, 
extent and/or habitat, making it highly vulnerable to extirpation in the state 

52 At risk because of very limited and potentially declining numbers, extent and/or 
habitat, making it vulnerable to extirpation in the state 

53 Potentially at risk because of limited and potentially declining numbers, extent 
and/or habitat, even though it may be abundant in some areas 

54 Uncommon but not rare (although it may be rare in parts of its range), and usually 
widespread. Apparently not vulnerable in most of its range, but possibly cause for 
long-term concern 

55 Common, widespread, and abundant (although it may be rare in parts of its 
range). Not vulnerable in most of its range 

Combination Ra n k s 

G#G# or S#S# Range Rank — A numeric range rank (e.g., G2G3) used to indicate uncertainty about 
the exact status of a taxon 

Qualifiers 

NR Not ranked 

Q Questionable taxonomy that may reduce conservation priority — Distinctiveness of 

this entity as a taxon at the current level is questionable; resolution of this uncertainty 
may result in change from a species to a subspecies or hybrid, or inclusion of this taxon 
in another taxon, with the resulting taxon having a lower-priority (numerically higher) 
conservation status rank 



Appendix A - 1 



X Presumed Extinct — Species believed to be extinct throughout its range. Not located 

despite intensive searches of historical sites and other appropriate habitat, and virtually 
no likelihood that it will be rediscovered 

H Possibly Extinct — Species known from only historical occurrences, but may never- the- 

less still be extant; further searching needed 

U Unrankable — Species currently unrankable due to lack of information or due to substan- 

tially conflicting information about status or trends 

HYB Hybrid — Entity not ranked because it represents an interspecific hybrid and not a species 

? Inexact Numeric Rank — Denotes inexact numeric rank 

C Captive or Cultivated Only — Species at present is extant only in captivity or 

cultivation, or as a reintroduced population not yet established 

A Accidental — Species is accidental or casual in Montana, in other words, infrequent and 

outside usual range. Includes species (usually birds or butterflies) recorded once or only a 
few times at a location. A few of these species may have bred on the one or two occa- 
sions they were recorded 

Z Zero Occurrences — Species is present but lacking practical conservation concern in 

Montana because there are no definable occurrences, although the taxon is native and 
appears regularly in Montana 

P Potential — Potential that species occurs in Montana but no extant or historic occurrences 

are accepted 

R Reported — Species reported in Montana but without a basis for either accepting or 

rejecting the report, or the report not yet reviewed locally. Some of these are very recent 
discoveries for which the program has not yet received first-hand information; others are 
old, obscure reports 

SYN Synonym — Species reported as occurring in Montana, but the Montana Natural Heritage 

Program does not recognize the taxon; therefore the species is not assigned a rank 

* A rank has been assigned and is under review. Contact the Montana Natural Heritage 

Program for assigned rank 

B Breeding — Rank refers to the breeding population of the species in Montana 

N Nonbreeding — Rank refers to the non-breeding population of the species in Montana 



Appendix A - 2 



Appendix B. Distribution Maps for Bats in Montana 






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Appendix B - 11 



Appendix C. Application of Oregon Bat Grid to Montana 
- Cell Ownership and Accessibility 



Cell Count per Forest by Ranger District 



FOREST 


DISTRICT 


NUMBER 
OF CELLS 


BEAVERHEAD-DEERLODGE 








Butte 


6 




Butte- Jefferson 


2 




Dillon 


32 




Dillon-WiseRiver 


2 




Jefferson 


19 




Jefferson-Madison 


1 




Madison 


26 




Pintler 


22 




Pintler-WiseRiver 


1 




Wisdom 


19 




Wisdom-Dillon 


1 




Wisdom-WiseRiver 


1 




WiseRiver 


18 




WiseRiver- Wisdom-Dillon 


1 


BITTERROOT 








Darby 


15 




Darby-Stevensville 


2 




Stevensville 


13 




Sula 


10 




WestFork 


19 




WestFork-Darby 


1 


CUSTER 








Ashland 


22 




Beartooth 


24 




Sioux 


4 


FLATHEAD 








GlacierView 


16 




HungryHorse 


17 




HungryHorse-SpottedBear 


1 




SpottedBear 


37 




SwanLake 


19 




SwanLake-HungryHorse 


1 




SwanLake-HungryHorse-SpottedBear 


1 




TallyLake 


12 


GALLATIN 








BigTimber 


12 




B igTimber-Gar diner 


2 




Big Timber-Livingston 


2 




Bozeman 


18 




Bozeman-Livingston 


2 




Gardiner 


19 




HebgenLake 


19 




Livingston 


11 




Livingston-Gardiner 


1 



Appendix C - 1 



FOREST 


DISTRICT 


NUMBER 
OF CELLS 


HELENA 








Helena 


14 




Helena-Townsend 


3 




Lincoln 


16 




Townsend 


in 


KOOTENAI 








Cabinet 


26 




Fortine 


17. 




Libbv 


29 




Rexford 


15 




ThreeRivers 


32 


LEWIS AND CLARK 








BeltCreek 


7 




Judith 


14 




Judith-Mussel shell 


2 




White Suphur Sprine 


11 




White Sulphur Spring-Musselshell 


2 




Musselshell 


9 




RockvMountain 


34 


LOLO 








Missoula 


IX 




Ninemile 


17 




Plains/ThompsonFalls 


20 




SeelevLake 


17. 




Superior 


28 








TOTAL 




787. 



Mixed Forest Cells 



FORESTS 


DISTRICTS 


CELL 
COUNT 


Beaverhead-Deerlodge_Bitterroot 


Pintler_Darby 


1 


Beaverhead-Deerlodge_Bitterroot 


Pintler_Darby-Sula 


1 


Beaverhead-Deerlodge_Bitterroot 


Wisdom_Sula 


1 


Beaverhead-Deerlodge_Lolo 


Pintler_Missoula 


3 


Custer_Gallatin 


B eartooth_Gardiner 


3 


Gallatin_LewisandClark 


Livingston_Musselshell 


1 


Helena_Beaverhead-Deerlodge 


Helena_Jefferson 


1 


Helena_Beaverhead-Deerlodge 


Helena_Pintler 


1 


Total 




12 



Appendix C - 2 



Forest and Other Public Lands Mixed Cells 



FOREST AND OTHER PUBLIC LANDS 


DISTRICT(S) 


CELL 


Beaverhead-Deerlodge_BLM 


Dillon 


8 


Beaverhead-Deerlodge_BLM 


Madison 


2 


Beaverhead-Deerlodge_BLM 


WiseRiver 


1 


B eaverhead-Deerlodge_S tateLands 


Madison 


4 


B eaverhead-Deerlodge_S tateLands 


WiseRiver 


1 


Custer_BLM 


Beartooth 


4 


Flathead_S tateLands 


GlacierView-TallyLake 


1 


Gallatin_S tateLands 


Gardiner-Livingston 


1 


Helena_BLM 


Helena 


1 


Helena_BLM 


Townsend 


2 


Helena_S tateLands 


Helena 


2 


Total 




27 



Appendix C - 3 



Appendix D. Site Locations for USFS 2006 Bat Surveys 









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EUMA (all Acoustic) 


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Appendix D - 2 



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cu 
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MYVO (M), LANO (M), EPFU (M), 
LACI (M); Acoustic data not yet 
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73 

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Appendix D - 5 



Appendix E. Documented Species List per Forest/District 



BUTTE 
Beaverhead/Deerlodge 

Big Brown Bat 

Silver-haired Bat 

Hoary Bat 

Western Small-footed Myotis 

Long-eared Myotis 

Little Brown Myotis 



Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 



Dillon 

Big Brown Bat 
Hoary Bat 

California Myotis* 
Western Small-footed Myotis 
Long-eared Myotis 
Little Brown Myotis 
Fringed Myotis 
Long-legged Myotis 

Jefferson 

Big Brown Bat 

Silver-haired Bat 

Hoary Bat 

Western Small-footed Myotis 

Long-eared Myotis 

Little Brown Myotis 

Fringed Myotis 

Madison 

Townsend's Big-eared Bat 
Long-eared Myotis 



Eptesicus fuscus 
Lasiurus cinereus 
Myotis californicus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis thysanodes 
Myotis volans 



Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis thysanodes 



Corynorhinus townsendii 
Myotis evotis 



Pintler (Philipsburg/Deer Lodge) 



Big Brown Bat 
Silver-haired Bat 
Hoary Bat 

Western Small-footed Myotis 
Long-eared Myotis 
Little Brown Myotis 
Long-legged Myotis 
Yuma Myotis 

Wisdom 

Little Brown Myotis 



Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis volans 
Myotis yumanensis+ 



Myotis lucifugus 



Appendix E - 1 



BITTERROOT 
Darby 

Townsend's Big-eared Bat 

Big Brown Bat 

Western Small-footed Myotis 

Long-eared Myotis 

Little Brown Myotis 

Stevensville 

Townsend's Big-eared Bat 
Little Brown Myotis 

Sula 

Big Brown Bat 
Silver-haired Bat 
California Myotis 
Long-eared Myotis 
Long-legged Myotis 



Corynorhinus townsendii 
Eptesicus fuscus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 



Corynorhinus townsendii 
Myotis lucifugus 



Eptesicus fuscus 
Lasionycteris noctivagans 
Myotis californicus 
Myotis evotis 
Myotis volans 



West Fork 

Little Brown Myotis 



Myotis lucifugus 



CUSTER 
Ashland 

Townsend's Big-eared Bat 

Big Brown Bat 

Spotted Bat 

Silver-haired Bat 

Hoary Bat 

Western Small-footed Myotis 

Long-eared Myotis 

Little Brown Myotis 

Long-legged Myotis 



Corynorhinus townsendii 
Eptesicus fuscus 
Euderma maculatum 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



Beartooth 

Pallid Bat 

Townsend's Big-eared Bat 

Big Brown Bat 

Spotted Bat 

Silver-haired Bat 

Hoary Bat 

California Myotis* 

Western Small-footed Myotis 

Long-eared Myotis 

Little Brown Myotis 

Fringed Myotis 

Long-legged Myotis 



Antrozous pallidus 
Corynorhinus townsendii 
Eptesicus fuscus 
Euderma maculatum 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis californicus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis thysanodes 
Myotis volans 



Appendix E - 2 



Sioux 

Townsend's Big-eared Bat 
Big Brown Bat 
Silver-haired Bat 
Hoary Bat 

Western Small-footed Myotis 
Long-eared Myotis 
Little Brown Myotis 
Long-legged Myotis 



Corynorhinus townsendii 
Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



FLATHEAD 
Hungry Horse 

Big Brown Bat 
Little Brown Myotis 

Spotted Bear 

Long-legged Myotis 



Eptesicus fuscus 
Myotis lucifugus 



Myotis volans 



Swan Lake 

Hoary Bat 
California Myotis 
Long-eared Myotis 
Little Brown Myotis 
Long-legged Myotis 



Lasiurus cinereus 
Myotis californicus 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



Tally Lake 

Big Brown Bat 
Silver-haired Bat 
Hoary Bat 
California Myotis 
Long-eared Myotis 
Long-legged Myotis 



Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis californicus 
Myotis evotis 
Myotis volans 



GALLATIN 
Big Timber 

Big Brown Bat 

Silver-haired Bat 

Hoary Bat 

Western Small-footed Myotis 

Little Brown Myotis 



Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis lucifugus 



Bozeman 

Big Brown Bat 

Silver-haired Bat 

Hoary Bat 

Western Small-footed Myotis 

Long-eared Myotis 

Little Brown Myotis 

Long-legged Myotis 



Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



Appendix E - 3 



Gardiner 

Big Brown Bat 
Hoary Bat 
Long-eared Myotis 
Little Brown Myotis 

Hebgen Lake 

Little Brown Myotis 

Livingston 

Little Brown Myotis 
Long-legged Myotis 



Eptesicus fuscus 
Lasiurus cinereus 
Myotis evotis 
Myotis lucifugus 



Myotis lucifugus 



Myotis lucifugus 
Myotis volans 



HELENA 
Helena 

Townsend's Big-eared Bat 
Big Brown Bat 
Silver-haired Bat 
Hoary Bat 

Western Small-footed Myotis 
Long-eared Myotis 
Little Brown Myotis 
Fringed Myotis 
Long-legged Myotis 



Corynorhinus townsendii 
Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis thysanodes 
Myotis volans 



Lincoln 

Big Brown Bat 
Silver-haired Bat 
Western Small-footed Myotis 
Long-eared Myotis 
Fringed Myotis 
Long-legged Myotis 



Eptesicus fuscus 
Lasionycteris noctivagans 
Myotis ciliolabrum 
Myotis evotis 
Myotis thysanodes 
Myotis volans 



Townsend 

Townsend's Big-eared Bat 
Big Brown Bat 
Silver-haired Bat 
Hoary Bat 

Western Small-footed Myotis 
Long-eared Myotis 
Little Brown Myotis 
Fringed Myotis 
Long-legged Myotis 
Yuma Myotis 



Corynorhinus townsendii 
Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis thysanodes 
Myotis volans 
Myotis yumanensis+ 



Appendix E - 4 



KOOTENAI 
Cabinet 

Townsend's Big-eared Bat 
Big Brown Bat 
Silver-haired Bat 
Hoary Bat 
California Myotis 
Western Small-footed Myotis 
Long-eared Myotis 
Little Brown Myotis 
Long-legged Myotis 
Yuma Myotis 



Corynorhinus townsendii 
Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis californicus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis volans 
Myotis yumanensis+ 



Fortine 

Townsend's Big-eared Bat 
Big Brown Bat 
Silver-haired Bat 
Hoary Bat 
California Myotis 
Long-eared Myotis 
Little Brown Myotis 
Long-legged Myotis 

Libby 

Pallid Bat 

Townsend's Big-eared Bat 

Big Brown Bat 

Silver-haired Bat 

Hoary Bat 

California Myotis 

Western Small-footed Myotis 

Long-eared Myotis 

Little Brown Myotis 

Fringed Myotis 

Long-legged Myotis 

Yuma Myotis 

Rexford 

Townsend's Big-eared Bat 
Big Brown Bat 
Silver-haired Bat 
Hoary Bat 
California Myotis 
Western Small-footed Myotis 
Long-eared Myotis 
Little Brown Myotis 
Long-legged Myotis 



Corynorhinus townsendii 
Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis californicus 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



Antrozous pallidus 
Corynorhinus townsendii 
Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis californicus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis thysanodes 
Myotis volans 
Myotis yumanensis+ 



Corynorhinus townsendii 
Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis californicus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



Appendix E - 5 



Three Rivers 

Townsend's Big-eared Bat 
Big Brown Bat 
Silver-haired Bat 
Hoary Bat 
California Myotis 
Long-eared Myotis 
Little Brown Myotis 
Long-legged Myotis 

LEWIS AND CLARK 
Belt Creek 

Townsend's Big-eared Bat 

Judith 

Big Brown Bat 

Silver-haired Bat 

Hoary Bat 

Western Small-footed Myotis 

Long-eared Myotis 

Little Brown Myotis 

Long-legged Myotis 

White Sulphur Spring 

Long-eared Myotis 
Fringed Myotis 
Yuma Myotis 



Corynorhinus townsendii 
Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis californicus 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



Corynorhinus townsendii 

Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



Myotis evotis 
Myotis thysanodes 
Myotis yumanensis+ 



Musselshell 

Big Brown Bat 

Silver-haired Bat 

Hoary Bat 

Western Small-footed Myotis 

Long-eared Myotis 

Little Brown Myotis 

Long-legged Myotis 



Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



Rocky Mountain 

Silver-haired Bat 
Hoary Bat 
Long-eared Myotis 
Little Brown Myotis 
Long-legged Myotis 
Yuma Myotis 



Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis evotis 
Myotis lucifugus 
Myotis volans 
Myotis yumanensis+ 



Appendix E - 6 



LOLO 
Missoula 

Big Brown Bat 
Silver-haired Bat 
Hoary Bat 
California Myotis 
Long-eared Myotis 
Little Brown Myotis 

Plains/Thompson Falls 

Townsend's Big-eared Bat 
Silver-haired Bat 
California Myotis 
Long-eared Myotis 
Long-legged Myotis 

Superior 

Townsend's Big-eared Bat 
Big Brown Bat 
Silver-haired Bat 
Hoary Bat 
California Myotis 
Western Small-footed Myotis 
Long-eared Myotis 
Little Brown Myotis 
Long-legged Myotis 



Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis californicus 
Myotis evotis 
Myotis lucifugus 



Corynorhinus townsendii 
Lasionycteris noctivagans 
Myotis californicus 
Myotis evotis 
Myotis volans 



Corynorhinus townsendii 
Eptesicus fuscus 
Lasionycteris noctivagans 
Lasiurus cinereus 
Myotis californicus 
Myotis ciliolabrum 
Myotis evotis 
Myotis lucifugus 
Myotis volans 



* tentative identification 

+ species presence in the state in question 



Appendix E - 7 



Appendix F. Site Occupancy and Detection Probability 

Analysis 



Psi = 0.3 & p = 0.2 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 


4 


8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.269 


0.147 


0.349 


0.146 


0.086 


0.083 


0.092 


0.084 


- 



50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 100 50 50 50 25 

S 2 4 2 4 8 

Roost 13 

SE 0.335 0.227 0.394 0.231 0.137 



25 





8 





13 


50 


0.139 


. 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.388 0.302 0.383 0.309 

Psi = 0.3 & p = 0.4 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 




8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.081 


0.054 


0.156 


0.053 


0.063 


0.061 


0.086 


0.081 


- 



50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 100 50 50 50 25 25 

S 2 4 2 4 8 8 

Roost 13 13 

SE 0.151 0.082 0.245 0.080 0.094 0.082 

25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 
M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.240 0.133 0.331 0.143 





50 



Appendix F - 1 



Psi = 0.3 & p = 0.6 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 




100 


100 




50 


50 


25 


25 





* 


» 


4 


2 




8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.043 


0.048 


0.065 


0.043 


0.061 


0.057 


0.091 


0.075 


- 



50 Sampling Days (1 day = 


: 4 grid cell surveys or 0.5 roost 


surveys) 






M 100 


50 50 


50 


25 


25 





S 2 


4 2 




8 


8 





Roost 





13 





13 


50 


SE 0.065 


0.063 0.114 


0.061 


0.094 


0.082 


■ 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.113 0.095 0.212 0.094 

Psi = 0.3 & p = 0.8 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 




8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.036 


0.044 


0.057 


0.042 


0.064 


0.056 


0.090 


0.071 


- 



50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 100 50 50 50 25 

S 2 4 2 4 8 

Roost 13 

SE 0.057 0.066 0.069 0.060 0.089 



25 





8 





13 


50 


0.079 


. 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

. 4 2 4 » 

Roost 6 25 

SE 0.069 0.096 0.138 0.083 



Appendix F - 2 



Psi = 0.5 & p = 0.2 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 


4 


8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.214 


0.140 


0.270 


0.133 


0.099 


0.096 


0.101 


0.095 


- 



50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 100 50 50 50 25 25 

S 2424880 

Roost 13 13 50 

SE 0.269 0.195 0.318 0.194 0.142 0.135 

25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.321 0.248 0.346 0.254 

Psi = 0.5 & p = 0.4 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 
M 200 100 100 100 50 

S 2 4 2 4 8 

Roost 25 

SE 0.094 0.070 0.150 0.056 0.072 

50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 
M 100 50 50 50 25 

S 2 4 2 4 8 

Roost 13 

SE 0.149 0.093 0.198 0.088 0.097 

25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 
M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.200 0.135 0.258 0.129 



50 


25 


25 





8 


16 


16 





25 





25 


100 


0.064 


0.099 


0.090 


- 


25 









8 









13 


50 






0.098 


. 







Appendix F - 3 



Psi = 0.5 & p = 0.6 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





■ 




4 


2 




8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.050 


0.055 


0.069 


0.048 


0.070 


0.067 


0.099 


0.082 


- 



50 Sampling Days (1 day = 


: 4 grid cell surveys or 0.5 roost 


surveys) 






M 100 


50 50 


50 


25 


25 





S 2 


4 2 




8 


8 





Roost 





13 





13 


50 


SE 0.072 


0.076 0.111 


0.066 


0.100 


0.092 


- 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.111 0.102 0.163 0.097 



Psi = 0.5 & p = 0.8 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 




8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.043 


0.053 


0.052 


0.048 


0.069 


0.062 


0.097 


0.077 


0.088 



50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 100 50 50 50 25 

S 2 4 2 4 8 

Roost 13 

SE 0.055 0.069 0.076 0.067 0.101 



25 





8 





13 


50 


0.080 


0.088 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M S(i 25 25 25 

S 4 2 4 

Roost 6 25 



SE 




0.099 



0.107 



0.091 



0.127 



Appendix F - 4 



Psi = 0.7 & p = 0.2 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 


4 


8 




16 


16 





Roost 











25 





25 





25 


100 


SE 


0.191 


0.135 


0.230 


0.133 


0.099 


0.096 


0.098 


0.095 


- 



50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 100 50 50 50 25 

S 2 4 2 4 8 

Roost 13 

SE 0.227 0.174 0.263 0.176 0.133 



25 





8 





13 


50 


1.136 


. 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.261 0.209 0.290 0.211 

Psi = 0.7 & p = 0.4 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 


4 


8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.100 


0.062 


0.137 


0.061 


0.066 


0.064 


0.090 


0.084 


- 



50 Sampling 


Days (1 day = 


= 4 grid cell 


surveys or 0.5 roost 


surveys) 






M 


100 


50 


50 


50 


25 


25 





S 


2 


4 


2 


4 


8 


8 





Roost 











13 





13 


50 


SE 


0.135 


0.087 


0.170 


0.088 


0.092 


0.086 


- 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.171 0.126 0.210 0.126 



Appendix F - 5 



Psi = 0.7 & p = 0.6 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 




100 


100 


100 


50 


50 


25 


25 





* 


» 


4 


2 




8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.053 


0.049 


0.074 


0.048 


0.066 


0.061 


0.093 


0.082 


0.093 



50 Sampling Days (1 day = 


: 4 grid cell surveys or 0.5 roost 


surveys) 






M 100 


50 50 


50 


25 


25 





S 2 


4 2 




8 


8 





Roost 





13 





13 


50 


SE 0.074 


0.068 0.108 


0.065 


0.094 


0.081 


0.101 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.109 0.097 0.145 0.094 0.138 

Psi = 0.7 & p = 0.8 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 




4 


2 


4 


8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.036 


0.046 


0.051 


0.043 


0.063 


0.057 


0.097 


0.075 


0.126 



50 Sampling Days (1 day = 


: 4 grid cell surveys or 0.5 roost 


surveys) 






M 


100 


50 50 




25 


25 





■ 


' 


4 2 




8 


8 





Roost 








13 





13 


50 


SE 


0.049 


0.062 0.073 


0.060 


0.091 


0.076 


0.137 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.071 0.092 0.101 0.084 0.151 



Appendix F - 6 



Psi = 0.9 & p = 0.2 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 


4 


8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.143 


0.102 


0.174 


0.105 


0.075 


0.077 


0.068 


0.067 


- 



50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 100 50 50 50 25 25 

S 2424880 

Roost 13 13 50 

SE 0.175 0.119 0.203 0.128 0.101 0.098 

25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.201 0.155 0.242 0.158 9.856 

Psi = 0.9 & p = 0.4 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 




8 




16 


16 





Roost 











25 





25 





25 


100 


SE 


0.081 


0.054 


0.101 


0.055 


0.046 


0.047 


0.060 


0.061 


0.079 



50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 100 50 50 50 25 25 

S 2424880 

Roost 13 13 50 

SE 0.104 0.071 0.124 0.073 0.062 0.064 0.082 

25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.124 0.098 0.151 0.092 0.114 



Appendix F - 7 



Psi = 0.9 & p = 0.6 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 




100 


100 


100 


50 


50 


25 


25 





S 


» 


4 


2 




8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.049 


0.034 


0.064 


0.034 


0.043 


0.042 


0.059 


0.058 


0.135 



50 Sampling Days (1 day = 


: 4 grid cell surveys or 0.5 roost 


surveys) 






M 100 


50 50 


50 


25 


25 





S 2 


4 2 




8 


8 





Roost 





13 





13 


50 


SE 0.063 


0.048 0.082 


0.048 


0.059 


0.058 


0.147 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.080 0.068 0.106 0.067 0.158 

Psi = 0.9 & p = 0.8 
100 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 



M 


200 


100 


100 


100 


50 


50 


25 


25 





S 


2 


4 


2 




8 


8 


16 


16 





Roost 











25 





25 





25 


100 


SE 


0.027 


0.029 


0.038 


0.028 


0.042 


0.039 


0.059 


0.055 


0.091 



50 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 100 50 50 50 25 

S 2 4 2 4 8 

Roost 13 

SE 0.039 0.043 0.054 0.042 0.057 



25 





8 





13 


50 


0.057 


0.103 



25 Sampling Days (1 day = 4 grid cell surveys or 0.5 roost surveys) 

M 50 25 25 25 

S 2 4 2 4 

Roost 6 25 

SE 0.054 0.059 0.070 0.057 

Psi - Estimated Proportion of Sites Occupied (species specific) 

p - Estimated Probability of Detection (species specific) 

M - Multiple Sites (Cell Count) 

S - Number of Surveys per site (4 = one mist-net and three acoustic stations) 

Roost - Number of Roost sites surveyed (this would occur in conjunction with individual cell surveys) 

SE - Standard Error 



Appendix F - 8